1. Field of the Invention
The present invention relates to an apparatus and method of segmenting an image and/or receiving the segmented image in an image coding and/or decoding system, and more particularly, to an apparatus and method of adjust a resolution of an image to a second resolution, to segment the image of the second resolution, and to generate a segmentation image according to the segmented image and the image of the first resolution in a mixed raster content based coding and/or decoding system.
2. Description of the Related Art
Mixed Raster Content (MRC), defined in ITU-T T.44, is a standard for efficient document compression which can dramatically improve the compression/quality tradeoff as compared to traditional lossy image compression algorithms. MRC represents an image as a set of layers. In the most basic mode of MRC, a compound document with text and pictures is separated into three layers: a binary mask layer, a foreground layer and a background layer. The binary mask layer indicates the assignment of foreground as “1”, or background as “0” to each pixel. According to ITU-T T.44, it is recommended that text and line art be classified to the foreground layer, and pictures classified to the background.
MRC encoding includes a process of segmentation to differentiate text and graphics regions within an image and creates the binary mask layer described above. Typically, the foreground layer contains the colors of text, the background layer contains images and graphics, and the binary mask layer is used to represent the fine detail of text fonts. The quality of decoded image is heavily dependent on the segmentation algorithm because binary mask defines the shape of characters, and because incorrect segmentation can cause distortion in the decoded image.
Although segmentation is a critical step in MRC encoding, the standard ITU-T T. 44 does not define the segmentation method. The standard defines only the structure of MRC document decoder, so the segmentation algorithm may be independently optimized for best performance.
In general, the computation time of segmentation is highly dependent on the number of pixels in the input image. Especially when high resolution images are processed, it is most often necessary to consider computation time improvements.